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Remote estimation of target height from unmanned aerial vehicle (UAV) images

Authors :
Andrea Tonini
Mauro Castelli
Paula Redweik
Marco Painho
Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
Source :
Remote sensing, vol. 12, no. 21 ( 3602), pp. 1-24, 2020., Remote Sensing, Vol 12, Iss 3602, p 3602 (2020), Remote Sensing; Volume 12; Issue 21; Pages: 3602
Publication Year :
2021
Publisher :
MDPI, 2021.

Abstract

Tonini, A., Redweik, P., Painho, M., & Castelli, M. (2020). Remote estimation of target height from unmanned aerial vehicle (Uav) images. Remote Sensing, 12(21), 1-24. [3602]. https://doi.org/10.3390/rs12213602 This paper focuses on how the height of a target can be swiftly estimated using images acquired by a digital camera installed into moving platforms, such as unmanned aerial vehicles (UAVs). A pinhole camera model after distortion compensation was considered for this purpose since it does not need extensive processing nor vanishing lines. The pinhole model has been extensively employed for similar purposes in past studies but mainly focusing on fixed camera installations. This study analyzes how to tailor the pinhole model for gimballed cameras mounted into UAVs, considering camera parameters and flight parameters. Moreover, it indicates a solution that foresees correcting only a few needed pixels to limit the processing overload. Finally, an extensive analysis was conducted to define the uncertainty associated with the height estimation. The results of this analysis highlighted interesting relationships between UAV‐to‐target relative distance, camera pose, and height uncertainty that allow practical exploitations of the proposed approach. The model was tested with real data in both controlled and uncontrolled environments, the results confirmed the suitability of the proposed method and outcomes of the uncertainty analysis. Finally, this research can open consumer UAVs to innovative applications for urban surveillance. publishersversion published

Details

Language :
English
ISSN :
20724292
Database :
OpenAIRE
Journal :
Remote sensing, vol. 12, no. 21 ( 3602), pp. 1-24, 2020., Remote Sensing, Vol 12, Iss 3602, p 3602 (2020), Remote Sensing; Volume 12; Issue 21; Pages: 3602
Accession number :
edsair.doi.dedup.....ff130ad075ea42fa7711fb857f69743c